Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. Given the location and geometry of an initial landslide body (i.e., landslide polygon), MWR models the downslope progression of the runout process and evolves the underlying terrain. Runout behavior is controlled by mass continuity, topography, and rules for erosion and deposition, which can be informed from field observations. MWR includes a calibration utility that uses a Markov Chain Monte Carlo algorithm to sample model parameter space and tune the model to match observed patterns of landslide runout extent, deposition and erosion. Output from the calibration utility informs probabilistic implementation of MWR. Here we demonstrate calibrated model performance relative to a range of observed runout phenomena and terrain, including debris flows in channelized, low-energy-dissipation terrains and debris avalanches on open-slope, moderate, to high-energy-dissipation terrains. We test model ability to predict runout probability at a case study site using parameters that were determined through calibration to a different site. Finally, we show how to use a calibrated MWR model to determine runout-probability from an expert-defined, potentially unstable slope and a landslide hazard map.;
